Telco Customer Churn

Focused customer retention programs

About Dataset

Context

"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets]

Content

Each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents Inspiration To explore this type of models and learn more about the subject.

New version from IBM:

https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

import require libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.preprocessing import LabelEncoder, minmax_scale,StandardScaler
from sklearn.metrics import (
    confusion_matrix,
    classification_report,
    roc_curve,
    auc,
    RocCurveDisplay,accuracy_score
)
import optuna
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import warnings
warnings.simplefilter("ignore")

data = pd.read_csv(r"C:\Users\hardi\Downloads\WA_Fn-UseC_-Telco-Customer-Churn (1).csv")

print("Top 10 rows :\n")

data.head(10)

Top 10 rows :

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
5	9305-CDSKC	Female	0	No	No	8	Yes	Yes	Fiber optic	No	...	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	99.65	820.5	Yes
6	1452-KIOVK	Male	0	No	Yes	22	Yes	Yes	Fiber optic	No	...	No	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	89.10	1949.4	No
7	6713-OKOMC	Female	0	No	No	10	No	No phone service	DSL	Yes	...	No	No	No	No	Month-to-month	No	Mailed check	29.75	301.9	No
8	7892-POOKP	Female	0	Yes	No	28	Yes	Yes	Fiber optic	No	...	Yes	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	104.80	3046.05	Yes
9	6388-TABGU	Male	0	No	Yes	62	Yes	No	DSL	Yes	...	No	No	No	No	One year	No	Bank transfer (automatic)	56.15	3487.95	No

10 rows × 21 columns

print("Last 10 rows :\n")
data.tail(10)

Last 10 rows :

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
7033	9767-FFLEM	Male	0	No	No	38	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Credit card (automatic)	69.50	2625.25	No
7034	0639-TSIQW	Female	0	No	No	67	Yes	Yes	Fiber optic	Yes	...	Yes	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	102.95	6886.25	Yes
7035	8456-QDAVC	Male	0	No	No	19	Yes	No	Fiber optic	No	...	No	No	Yes	No	Month-to-month	Yes	Bank transfer (automatic)	78.70	1495.1	No
7036	7750-EYXWZ	Female	0	No	No	12	No	No phone service	DSL	No	...	Yes	Yes	Yes	Yes	One year	No	Electronic check	60.65	743.3	No
7037	2569-WGERO	Female	0	No	No	72	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Bank transfer (automatic)	21.15	1419.4	No
7038	6840-RESVB	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	...	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.5	No
7039	2234-XADUH	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	...	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.9	No
7040	4801-JZAZL	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	8361-LTMKD	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.6	Yes
7042	3186-AJIEK	Male	0	No	No	66	Yes	No	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.5	No

10 rows × 21 columns

Total_Rows = data.shape[0]
Total_Cols = data.shape[1]
print("Total Rows is :",Total_Rows)
print("Total columns is :", Total_Cols)

Total Rows is : 7043
Total columns is : 21

print( "\n Information about the Titanic dataset : \n " )
data.info()

 Information about the Titanic dataset : 
 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

Columns distribution

feature_cols =  data.columns.tolist()
print("Total columns is here :\n", data.columns.tolist())

Total columns is here :
 ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn']

numerical_col = data.select_dtypes(include=["int64","float64"]).columns
print("Total Numerical columns list is here :\n", numerical_col)
print("\nTotal Numerical columns is here :\n" ,numerical_col.value_counts().sum())

Total Numerical columns list is here :
 Index(['SeniorCitizen', 'tenure', 'MonthlyCharges'], dtype='object')

Total Numerical columns is here :
 3

categorical_col = data.select_dtypes(include=["O"]).columns
print("Total categorical columns  list is here :\n", categorical_col)
print("\nTotal categorical columns is here :\n" ,categorical_col.value_counts().sum())

Total categorical columns  list is here :
 Index(['customerID', 'gender', 'Partner', 'Dependents', 'PhoneService',
       'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup',
       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',
       'Contract', 'PaperlessBilling', 'PaymentMethod', 'TotalCharges',
       'Churn'],
      dtype='object')

Total categorical columns is here :
 18

basic statistics about the data :

data.describe().T

	count	mean	std	min	25%	50%	75%	max
SeniorCitizen	7043.0	0.162147	0.368612	0.00	0.0	0.00	0.00	1.00
tenure	7043.0	32.371149	24.559481	0.00	9.0	29.00	55.00	72.00
MonthlyCharges	7043.0	64.761692	30.090047	18.25	35.5	70.35	89.85	118.75

data.describe(include="all").T

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
customerID	7043	7043	7590-VHVEG	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
gender	7043	2	Male	3555	NaN	NaN	NaN	NaN	NaN	NaN	NaN
SeniorCitizen	7043.0	NaN	NaN	NaN	0.162147	0.368612	0.0	0.0	0.0	0.0	1.0
Partner	7043	2	No	3641	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Dependents	7043	2	No	4933	NaN	NaN	NaN	NaN	NaN	NaN	NaN
tenure	7043.0	NaN	NaN	NaN	32.371149	24.559481	0.0	9.0	29.0	55.0	72.0
PhoneService	7043	2	Yes	6361	NaN	NaN	NaN	NaN	NaN	NaN	NaN
MultipleLines	7043	3	No	3390	NaN	NaN	NaN	NaN	NaN	NaN	NaN
InternetService	7043	3	Fiber optic	3096	NaN	NaN	NaN	NaN	NaN	NaN	NaN
OnlineSecurity	7043	3	No	3498	NaN	NaN	NaN	NaN	NaN	NaN	NaN
OnlineBackup	7043	3	No	3088	NaN	NaN	NaN	NaN	NaN	NaN	NaN
DeviceProtection	7043	3	No	3095	NaN	NaN	NaN	NaN	NaN	NaN	NaN
TechSupport	7043	3	No	3473	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StreamingTV	7043	3	No	2810	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StreamingMovies	7043	3	No	2785	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Contract	7043	3	Month-to-month	3875	NaN	NaN	NaN	NaN	NaN	NaN	NaN
PaperlessBilling	7043	2	Yes	4171	NaN	NaN	NaN	NaN	NaN	NaN	NaN
PaymentMethod	7043	4	Electronic check	2365	NaN	NaN	NaN	NaN	NaN	NaN	NaN
MonthlyCharges	7043.0	NaN	NaN	NaN	64.761692	30.090047	18.25	35.5	70.35	89.85	118.75
TotalCharges	7043	6531		11	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Churn	7043	2	No	5174	NaN	NaN	NaN	NaN	NaN	NaN	NaN

data.describe(include="O").T

	count	unique	top	freq
customerID	7043	7043	7590-VHVEG	1
gender	7043	2	Male	3555
Partner	7043	2	No	3641
Dependents	7043	2	No	4933
PhoneService	7043	2	Yes	6361
MultipleLines	7043	3	No	3390
InternetService	7043	3	Fiber optic	3096
OnlineSecurity	7043	3	No	3498
OnlineBackup	7043	3	No	3088
DeviceProtection	7043	3	No	3095
TechSupport	7043	3	No	3473
StreamingTV	7043	3	No	2810
StreamingMovies	7043	3	No	2785
Contract	7043	3	Month-to-month	3875
PaperlessBilling	7043	2	Yes	4171
PaymentMethod	7043	4	Electronic check	2365
TotalCharges	7043	6531		11
Churn	7043	2	No	5174

plt.figure(figsize=(10,6))
sns.pairplot(data)
plt.tight_layout()
plt.show()

<Figure size 1000x600 with 0 Axes>

correlation = data.corr(numeric_only=True)

plt.figure(figsize=(43,35))
sns.set(font_scale=5.0)
sns.heatmap(correlation ,linewidths="4" ,annot=True ,cmap="coolwarm",       
            annot_kws={"size": 50}, fmt=".2f",)
plt.title("Correlation Metrix" , fontsize=50)
plt.tight_layout()
plt.show()

# Histograms each numerical col

for col in numerical_col:
    plt.figure(figsize=(6,2))
    sns.histplot(x=data[col],color="gold",kde=True ,bins=30)
    plt.title(f"Histogram plot of {col}")
    plt.show()

plt.figure(figsize=(15, len(categorical_col)*4))

for i, col in enumerate(categorical_col, 1):
    plt.subplot(len(categorical_col), 2, i)
    sns.countplot(data=data, x=col, palette="Set2", order=data[col].value_counts().index)
    plt.title(f"Distribution of {col}")
    plt.xticks(rotation=45)
    plt.tight_layout()

plt.show()

sns.countplot(data=data, x=col, hue="Churn")

<Axes: xlabel='Churn', ylabel='count'>

for col in categorical_col:
    plt.figure(figsize=(6, 6))
    data[col].value_counts().plot.pie(
        autopct='%1.1f%%',
        startangle=90,
        shadow=True,
        colors=plt.cm.Paired.colors
    )
    plt.title(f"Pie Chart for {col}")
    plt.ylabel('') 
    plt.tight_layout()
    plt.show()

Preprocessing :

1. Handle null values:

data.drop(["customerID"], axis=1)

	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.5	No
7039	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.9	No
7040	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.6	Yes
7042	Male	0	No	No	66	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.5	No

7043 rows × 20 columns

data.isna().sum()

customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

2. Handle categorical cloumns

for i in categorical_col:
    lb = LabelEncoder()
    data[i] = lb.fit_transform(data[i])

categorical_col_update = data.select_dtypes(include=["O"]).columns
print(categorical_col_update)
print(data)

Index([], dtype='object')
      customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \
0           5375       0              0        1           0       1   
1           3962       1              0        0           0      34   
2           2564       1              0        0           0       2   
3           5535       1              0        0           0      45   
4           6511       0              0        0           0       2   
...          ...     ...            ...      ...         ...     ...   
7038        4853       1              0        1           1      24   
7039        1525       0              0        1           1      72   
7040        3367       0              0        1           1      11   
7041        5934       1              1        1           0       4   
7042        2226       1              0        0           0      66   

      PhoneService  MultipleLines  InternetService  OnlineSecurity  ...  \
0                0              1                0               0  ...   
1                1              0                0               2  ...   
2                1              0                0               2  ...   
3                0              1                0               2  ...   
4                1              0                1               0  ...   
...            ...            ...              ...             ...  ...   
7038             1              2                0               2  ...   
7039             1              2                1               0  ...   
7040             0              1                0               2  ...   
7041             1              2                1               0  ...   
7042             1              0                1               2  ...   

      DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \
0                    0            0            0                0         0   
1                    2            0            0                0         1   
2                    0            0            0                0         0   
3                    2            2            0                0         1   
4                    0            0            0                0         0   
...                ...          ...          ...              ...       ...   
7038                 2            2            2                2         1   
7039                 2            0            2                2         1   
7040                 0            0            0                0         0   
7041                 0            0            0                0         0   
7042                 2            2            2                2         2   

      PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  
0                    1              2           29.85          2505      0  
1                    0              3           56.95          1466      0  
2                    1              3           53.85           157      1  
3                    0              0           42.30          1400      0  
4                    1              2           70.70           925      1  
...                ...            ...             ...           ...    ...  
7038                 1              3           84.80          1597      0  
7039                 1              1          103.20          5698      0  
7040                 1              2           29.60          2994      0  
7041                 1              3           74.40          2660      1  
7042                 1              0          105.65          5407      0  

[7043 rows x 21 columns]

3. detect Outlines and Remove

target_column = "Churn"
numeric_cols = data.select_dtypes(include=np.number).columns.drop(target_column)

for col in numeric_cols:
    Q1 = data[col].quantile(0.25)
    Q3 = data[col].quantile(0.75)
    IQR = Q3 - Q1
    lower = Q1 - 1.5 * IQR
    upper = Q3 + 1.5 * IQR
    data = data[(data[col] >= lower) & (data[col] <= upper)]

for col in data:
    plt.figure(figsize=(6,2))
    sns.boxplot(x=data[col],color="green")
    plt.title(f"Boxplot of {col}")
    plt.show()
    

4. Features Scaling

scaler = StandardScaler()
 
data[numerical_col] = scaler.fit_transform(data[numerical_col])

5. Split into features and target col

x = data.drop(columns=["Churn"] , axis=1)
y = data.Churn

from sklearn.model_selection import train_test_split

x_train,x_test,y_train ,y_test = train_test_split(x,y,test_size=0.2 ,random_state=42)

Applying to Optuna

def objective(trial):
    try:
        params = {
            "max_depth": trial.suggest_int("max_depth", 3, 10),
            "n_estimators": trial.suggest_int("n_estimators", 50, 500),
            "min_samples_split": trial.suggest_int("min_samples_split", 2, 10)
        }
        model = RandomForestClassifier(**params, random_state=42)
        score = cross_val_score(model, x_train, y_train, cv=3, scoring="accuracy").mean()
        return score
    except Exception as e:
        print("Trial failed:", e)
        return 0.0

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=10)

best_params = study.best_params
final_rf = RandomForestClassifier(**best_params, random_state=42)
final_rf.fit(x_train, y_train)

[I 2025-06-10 11:25:19,738] A new study created in memory with name: no-name-14616425-ff30-4ae5-b49e-4533be3a6942
[I 2025-06-10 11:25:20,393] Trial 0 finished with value: 0.8092995133119602 and parameters: {'max_depth': 7, 'n_estimators': 97, 'min_samples_split': 10}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:22,117] Trial 1 finished with value: 0.8064812884825988 and parameters: {'max_depth': 6, 'n_estimators': 259, 'min_samples_split': 2}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:25,743] Trial 2 finished with value: 0.8076553282580141 and parameters: {'max_depth': 8, 'n_estimators': 466, 'min_samples_split': 5}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:27,655] Trial 3 finished with value: 0.8062462158433333 and parameters: {'max_depth': 7, 'n_estimators': 276, 'min_samples_split': 4}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:30,040] Trial 4 finished with value: 0.80671619569425 and parameters: {'max_depth': 7, 'n_estimators': 302, 'min_samples_split': 4}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:33,935] Trial 5 finished with value: 0.80671619569425 and parameters: {'max_depth': 8, 'n_estimators': 448, 'min_samples_split': 4}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:36,748] Trial 6 finished with value: 0.8081256389641585 and parameters: {'max_depth': 7, 'n_estimators': 392, 'min_samples_split': 2}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:38,265] Trial 7 finished with value: 0.79920065376993 and parameters: {'max_depth': 3, 'n_estimators': 345, 'min_samples_split': 4}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:39,267] Trial 8 finished with value: 0.8048371034286528 and parameters: {'max_depth': 9, 'n_estimators': 136, 'min_samples_split': 6}. Best is trial 0 with value: 0.8092995133119602.
[I 2025-06-10 11:25:40,373] Trial 9 finished with value: 0.8071860101175529 and parameters: {'max_depth': 7, 'n_estimators': 169, 'min_samples_split': 8}. Best is trial 0 with value: 0.8092995133119602.

RandomForestClassifier(max_depth=7, min_samples_split=10, n_estimators=97,
                       random_state=42)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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# FINAL MODEL TRAINING
final_rf = RandomForestClassifier(**best_params, random_state=42)
final_rf.fit(x_train, y_train)
y_pred = final_rf.predict(x_test)
y_proba = final_rf.predict_proba(x_test)[0:,1]

3️ CONFUSION MATRIX

plt.figure(figsize=(6, 4))
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt=".2f", cmap="Blues")
plt.title("Confusion Matrix")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.show()

print("\n Accuracy is :",accuracy_score(y_test,y_pred))

 Accuracy is : 0.8460093896713615

print("\nClassification Report:\n")
print(classification_report(y_test, y_pred))

Classification Report:

              precision    recall  f1-score   support

           0       0.87      0.95      0.91       827
           1       0.73      0.49      0.59       238

    accuracy                           0.85      1065
   macro avg       0.80      0.72      0.75      1065
weighted avg       0.84      0.85      0.83      1065

ROC CURVE

fpr, tpr, thresholds = roc_curve(y_test, y_proba)
roc_auc = auc(fpr, tpr)

plt.figure(figsize=(6, 4))
plt.plot(fpr, tpr, label=f"AUC = {roc_auc:.2f}")
plt.plot([0, 1], [0, 1], linestyle="--", color="Blue")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("ROC Curve")
plt.legend(loc="lower right")
plt.grid()
plt.show()

# FEATURE IMPORTANCE

importances = final_rf.feature_importances_
features = x_train.columns
indices = np.argsort(importances)[::-1]

plt.figure(figsize=(8, 6))
sns.barplot(x=importances[indices], y=features[indices])
plt.title("Feature Importance")
plt.xlabel("Importance Score")
plt.ylabel("Features")
plt.tight_layout()
plt.show()

Thank you